chapter  16
18 Pages

Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks

WithBARIS ATAKAN, OZGUR B. AKAN

Contents 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 16.2 Immune System and Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . 412

16.2.1 Biological Immune System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 16.2.2 Immune System-Inspired Cognitive Radio Networks . . . . . . . . . 414

16.3 Immune System-Inspired Spectrum Sensing and Management in Cognitive Radio Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 16.3.1 Immune System-Inspired Spectrum-Sensing Model

for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 16.3.2 Immune System-Inspired Spectrum Management Model

for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 16.4 Biological Task Allocation and Spectrum Sharing in Cognitive Radio

Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 16.4.1 Biological Task Allocation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419

16.4.2 Biological Task Allocation-Inspired Spectrum-Sharing Model for Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 420

16.5 Biological Switching-Inspired Spectrum Mobility Management in Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

16.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425

Cognitive radio (CR) is a promising wireless communication technology that aims to detect temporally unused spectrum bands by sensing its radio environment and communicate over these spectrum bands in order to enhance overall spectrum utilization. These objectives pose difficulties and have additional requirements such as self-organizing, self-adaptation, and scalability over conventional communication paradigms. Therefore, it is imperative to develop new communication models and algorithms. In nature, as a result of the natural evolution, biological systems have acquired great capabilities, which can be modeled and adopted for addressing the challenges in the CR domain. In this chapter, we explore the surprising similarities and mapping between cognitive radio network (CRN) architectures and natural biological systems. We introduce potential solution avenues from the biological systems toward addressing the challenges ofCRN such as spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility/handoff management. The objective of this chapter is to serve as a roadmap for the development of efficient scalable, adaptive, and self-organizing bio-inspired communication techniques for dynamic spectrum access in CRN architectures.